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David J. Foran
Researcher at Rutgers University
Publications - 135
Citations - 4164
David J. Foran is an academic researcher from Rutgers University. The author has contributed to research in topics: Image segmentation & Breast cancer. The author has an hindex of 33, co-authored 132 publications receiving 3464 citations. Previous affiliations of David J. Foran include University of Maryland, College Park & University of Medicine and Dentistry of New Jersey.
Papers
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Journal ArticleDOI
Robust Segmentation of Overlapping Cells in Histopathology Specimens Using Parallel Seed Detection and Repulsive Level Set
TL;DR: This paper proposes a novel algorithm that can reliably separate touching cells in hematoxylin-stained breast TMA specimens that have been acquired using a standard RGB camera and compares the pixel-wise accuracy provided by human experts with that produced by the new automated segmentation algorithm.
Patent
Collaborative diagnostic systems
TL;DR: The systems described in this article include tools for computer-assisted evaluation of objective characteristics of pathologies, along with human decision-making where substantial discretion is involved, and collaborative diagnosis may be provided through shared access to data and shared control over a diagnostic tool.
Journal ArticleDOI
Unsupervised segmentation based on robust estimation and color active contour models
TL;DR: This paper investigates the design, development, and implementation of a robust color gradient vector flow (GVF) active contour model for performing segmentation, using a database of 1791 imaged cells and shows the results were superior to the other unsupervised approaches, and comparable with supervised segmentation.
Journal ArticleDOI
AI in Medical Imaging Informatics: Current Challenges and Future Directions
Andreas S. Panayides,Amir A. Amini,Nenad Filipovic,Ashish Sharma,Sotirios A. Tsaftaris,Alistair A. Young,David J. Foran,Nhan Do,Spyretta Golemati,Tahsin Kurc,Kun Huang,Konstantina S. Nikita,Ben P. Veasey,Michalis Zervakis,Joel H. Saltz,Constantinos S. Pattichis +15 more
TL;DR: Integrative analytics approaches driven by associate research branches highlighted in this study promise to revolutionize imaging informatics as known today across the healthcare continuum for both radiology and digital pathology applications.
Proceedings ArticleDOI
Multiple Class Segmentation Using A Unified Framework over Mean-Shift Patches
TL;DR: This paper achieves multiple class object-based segmentation using the appearance and bag of keypoints models integrated over mean-shift patches using a novel affine invariant descriptor to model the spatial relationship of key points and apply the elliptical Fourier descriptor to describe the global shapes.